Trends in cardiovascular disease incidence among 22 million people in the UK over 20 years: population based study

Abstract Objective To investigate the incidence of cardiovascular disease (CVD) overall and by age, sex, and socioeconomic status, and its variation over time, in the UK during 2000-19. Design Population based study. Setting UK. Participants 1 650 052 individuals registered with a general practice contributing to Clinical Practice Research Datalink and newly diagnosed with at least one CVD from 1 January 2000 to 30 June 2019. Main outcome measures The primary outcome was incident diagnosis of CVD, comprising acute coronary syndrome, aortic aneurysm, aortic stenosis, atrial fibrillation or flutter, chronic ischaemic heart disease, heart failure, peripheral artery disease, second or third degree heart block, stroke (ischaemic, haemorrhagic, and unspecified), and venous thromboembolism (deep vein thrombosis or pulmonary embolism). Disease incidence rates were calculated individually and as a composite outcome of all 10 CVDs combined and were standardised for age and sex using the 2013 European standard population. Negative binomial regression models investigated temporal trends and variation by age, sex, and socioeconomic status. Results The mean age of the population was 70.5 years and 47.6% (n=784 904) were women. The age and sex standardised incidence of all 10 prespecified CVDs declined by 19% during 2000-19 (incidence rate ratio 2017-19 v 2000-02: 0.80, 95% confidence interval 0.73 to 0.88). The incidence of coronary heart disease and stroke decreased by about 30% (incidence rate ratios for acute coronary syndrome, chronic ischaemic heart disease, and stroke were 0.70 (0.69 to 0.70), 0.67 (0.66 to 0.67), and 0.75 (0.67 to 0.83), respectively). In parallel, an increasing number of diagnoses of cardiac arrhythmias, valve disease, and thromboembolic diseases were observed. As a result, the overall incidence of CVDs across the 10 conditions remained relatively stable from the mid-2000s. Age stratified analyses further showed that the observed decline in coronary heart disease incidence was largely restricted to age groups older than 60 years, with little or no improvement in younger age groups. Trends were generally similar between men and women. A socioeconomic gradient was observed for almost every CVD investigated. The gradient did not decrease over time and was most noticeable for peripheral artery disease (incidence rate ratio most deprived v least deprived: 1.98 (1.87 to 2.09)), acute coronary syndrome (1.55 (1.54 to 1.57)), and heart failure (1.50 (1.41 to 1.59)). Conclusions Despite substantial improvements in the prevention of atherosclerotic diseases in the UK, the overall burden of CVDs remained high during 2000-19. For CVDs to decrease further, future prevention strategies might need to consider a broader spectrum of conditions, including arrhythmias, valve diseases, and thromboembolism, and examine the specific needs of younger age groups and socioeconomically deprived populations.

In addition to the evidence from independent validation studies, we have compared disease incidence rates and trends over time with the literature and national healthcare audits, and performed careful validation and calibration of diagnostic code lists (details outlined in Supplementary text S2).We further performed sensitivity analyses using broader disease definitions (Figure S3), including diagnoses recorded on death certificates (Figure S4), using longer lookback periods (Figure S5), or restricting diagnoses recorded by specialists during hospital admission (Figure S6), and performed a series of disease-specific investigations into the validity of recorded diagnoses (Supplementary text S3).We found all of these to support the robustness and validity of the present data. References: 1 Herrett E, Thomas SL, Schoonen WM, Smeeth L, Hall AJ.

Supplementary text S2: Approach to diagnostic code list generation
To establish the diagnostic code lists used in the present study, we have established and followed the approach detailed below.First, we filtered diagnostic and procedure code dictionaries for a broad set of keywords and synonyms that reflect the condition of interest.Second, two independent clinicians selected relevant codes, under consideration of the study's research question and special requirements for specificity and sensitivity.A third independent expert was consulted to resolve disagreements.
Following that, we performed careful validation and calibration of diagnostic code lists through the following steps: • We compared and complemented diagnostic codes lists based on previous literature 1-12 , online clinical code repositories (eg.Opensafely 13 , BHF Data Science Centre 14 , or HDR UK phenotype libraries 15 ), and codes used in national healthcare audits [16][17][18] , to ensure completeness.• For each code, we extracted the number and frequency of occurrences within the population of interest and examined trends over time and differences between individual data sources (primary care, secondary care, death certificates and/or others).• For every condition, we compared calculated disease incidence rates and trends over time with the literature 1-4,6-12, [16][17][18][19][20][21][22][23][24][25][26][27][28][29] , and investigated methodological differences and clinical implications.• Finally, we performed a range of sensitivity analyses to assess the robustness of incidence calculations to changes in disease definitions, eg. by focusing on specific disease subtypes (eg.ischaemic stroke), broadening disease definitions (eg.cerebrovascular diseases), or restricting analyses to a more specific set of diagnostic codes.
For the present study, the disease definition panel included cardiologists, primary care physicians, epidemiologists, and health services researchers -all with extensive expertise in UK healthcare systems and electronic health record studies.In addition to the sensitivity analyses assessing the overall robustness and validity of disease definitions (Supplementary text S1), we also performed a series of disease-specific investigations into the validity of recorded diagnoses.Below we present two examples of such analyses.
To confirm the validity of heart failure cases included in our cohort, we performed the following sensitivity analyses.(a) case identification restricted to diagnostic codes included in national care monitoring programmes.While for our main analysis we intentionally expanded the diagnostic codes from the national audit programmes list with additional codes indicating a heart failure diagnosis, so as to ensure completeness; sensitivity analyses, restricting diagnostic codes to those used in the national audit programmes, found that 97% of patients in our cohort had a record heart failure used in the national clinical audit programmes, and led to no significant changes in the present results.(b) case identification restricted to diagnoses recorded in secondary care, or referred for specialist assessment or for echocardiography.We further found that 92% of patients included in our cohort had a heart failure diagnosis recorded in secondary care, or either a referral to specialist cardiology service or echocardiography.Sensitivity analyses using these more restrictive disease definitions led to modestly lower estimates of disease incidence, but no significant change in trends over time or difference by subgroups of age, sex, and socioeconomic status.
To confirm the validity of heart block diagnoses included in our cohort, we performed the following sensitivity analyses.(a) case identification restricted to diagnoses recorded during a hospital admission.These showed that 85% of patients with heart block also had a diagnosis of heart block recorded by specialists during a hospital admission.(b) case identification restricted to patients with a pacemaker implantation.These showed that 83% of individuals with heart block included in our study also had a pacemaker implanted.These findings are in line with expert expectations for this condition, as most but not all individuals with second or third degree heart block will require a pacemaker.Sensitivity analyses using these more restrictive disease definitions led to modestly lower estimates of disease incidence, but no significant change in trends over time or difference by subgroups of age, sex, and socioeconomic status.

Figure S1: Temporal trends in age at first diagnosis of cardiovascular diseases
Mean age at diagnosis between 2000 and 2019.'First cardiovascular disease' refers to the primary incidence of cardiovascular disease across the 10 conditions investigated in this study (that is the number of patients first diagnosed with a cardiovascular disease).Yearly estimates were smoothed using loess (locally estimated scatterplot smoothing) regression lines.S2.IRR = Incidence Rate Ratio, 95% CI = 95% Confidence Interval.

Incidence rates are presented as incidence rates per 100 000 person-years at risk and are age-sex-standardised to the 2013 European Standard Population. 'Any cardiovascular disease' refers to the primary incidence of cardiovascular disease across the 10 conditions investigated in this study (that is the number of patients first diagnosed with a cardiovascular disease). 'N' refers to the number of patients newly diagnosed with cardiovascular disease during the study period. Main analyses (in blue
) refer to diagnoses recorded in primary or secondary care.Sensitivity analyses (in grey) refer to diagnoses recorded in primary care, secondary care or death certificates.IRR = Incidence Rate Ratio, 95% CI = 95% Confidence Interval.Incidence rates are presented as incidence rates per 100 000 person-years at risk and are age-sex-standardised to the 2013 European Standard Population.'Any cardiovascular disease' refers to the primary incidence of cardiovascular disease across the 10 conditions investigated in this study (that is the number of patients first diagnosed with a cardiovascular disease).'N' refers to the number of patients newly diagnosed with cardiovascular disease during the study period.IRR = Incidence Rate Ratio, 95% CI = 95% Confidence Interval.The clinical code lists used to identify individual conditions presented in this manuscript are accessible in a machine-readable format (as a tab-delimited text file) on our GitHub repository (see https://github.com/nathalieconrad/CVD_incidence).

Cardiovascular prevention therapies refer to the percentage of patients with at least 2 prescriptions within 6 months after incident cardiovascular disease (CVD). Analyses were restricted to patients alive and registered with a general practice 30 days after diagnosis. ACE-inhibitors = Angiotensin-Converting Enzyme Inhibitors, ARB = Angiotensin II Receptor Blockers, NOAC = Nonvitamin K Antagonist Oral Anticoagulant. Detailed list of drug substances included in each drug class are presented in
Codes used in sensitivity analyses referring to broader disease definitions are labelled accordingly.
Note: Diagnostic codes should be imported, stored, and processed as text rather than integers to avoid automatic reformatting of long-digit numbers by certain software packages.

Figure S2 :
Figure S2: Incidence of cardiovascular diseases by region

Figure S3 :
Figure S3: Incidence of cardiovascular diseases over time from 2000-2019.Sensitivity analyses based on broader disease definitions.
vs. 2000-2002 Main analyses Sensitivity analyses, based on broader disease definitions

Figure S4 :
Figure S4: Incidence of cardiovascular diseases over time from 2000-2019.Sensitivity analyses including diagnoses recorded on death certificates vs. 2000-2002Excluding diagnoses recorded on death certificates Including diagnoses recorded on death certificates

Figure S5 :
Figure S5: Incidence of cardiovascular diseases over time from 2000-2019.Sensitivity analyses with longer lookback period

Figure S6 :
Figure S6: Incidence of cardiovascular diseases over time from 2000-2019.Sensitivity analyses restricted to diagnoses recorded during hospital admissions.
Figure S7: Initiation of cardiovascular prevention therapy within 6 months of diagnosis, among patients diagnosed with cardiovascular disease in the periods 2000-2002 and 2017-2019.
Validation and validity of diagnoses in the General Practice Research Database: a systematic review.British journal of clinical pharmacology 2010; 69: 4-14. 2 de Burgos-Lunar C, del Cura-González I, Cárdenas-Valladolid J, et al.Real-world data in primary care: validation of diagnosis of atrial fibrillation in primary care electronic medical records and estimated prevalence among consulting patients'.BMC Prim Care 2023; 24: 4. 3 Ruigómez A, Johansson S, Wallander M-A, Rodrı ́guez LAG.Incidence of chronic atrial fibrillation in general practice and its treatment pattern.Journal of Clinical Epidemiology 2002; 55: 358-63.Validation of the diagnosis of venous thromboembolism in general practice database studies.British Journal of Clinical Pharmacology 2000; 49: 591-6.9 Bibliography | CPRD.2023; published online Jan 11. https://www.cprd.com/bibliography(accessed Feb 2, 2023).10 Oyinlola JO, Campbell J, Kousoulis AA.Is real world evidence influencing practice?A systematic review of CPRD research in NICE guidances.BMC Health Services Research 2016; 16: 299.
4 Schaufelberger M, Ekestubbe S, Hultgren S, et al.Validity of heart failure diagnoses made in 2000-2012 in western Sweden.ESC Heart Fail 2019; 7: 36-45.5 Herrett E, Shah AD, Boggon R, et al.Completeness and diagnostic validity of recording acute myocardial infarction events in primary care, hospital care, disease registry, and national mortality records: cohort study.BMJ (Clinical research ed) 2013; 346: f2350.6 Hammad TA, McAdams MA, Feight A, Iyasu S, Dal Pan GJ.Determining the predictive value of Read/OXMIS codes to identify incident acute myocardial infarction in the General Practice Research Database.Pharmacoepidemiology and Drug Safety 2008; 17: 1197-201.7 Woodfield R, Grant I, Group UBSO, Group UBF-U and OW, Sudlow CLM.Accuracy of Electronic Health Record Data for Identifying Stroke Cases in Large-Scale Epidemiological Studies: A Systematic Review from the UK Biobank Stroke Outcomes Group.PLOS ONE 2015; 10: e0140533.8 Lawrenson R, Todd J-C, Leydon GM, Williams TJ, Farmer RDT.

Supplementary text S3: Examples of disease-specific sensitivity analyses investigating the validity of disease definitions
Specifically, the disease definition panel consisted of Prof. John McMurray, Prof. John Cleland, Prof. Naveed Sattar, Prof. Kazem Rahimi, Prof. Kamlesh Khunti and Dr. Nathalie Conrad.The clinical specialists on the panel are directly involved in treating patients with these conditions in the UK, both in primary care and secondary care settings; many have done so for over 30 years now and have personally witnessed changes in coding practices over the study period.By consensus, when designing this study, we chose disease definitions designed to optimise sensitivity and specificity i.e., unlikely to miss a substantial number of cases, but sufficiently restrictive to give valid estimates of incidence rates.

Ischaemic heart disease Peripheral arterial disease Supraventricular arrhythmias Stroke Valve disorders Venous thromboembolism or pulmonary embolism
Rate Ratio, 95% CI = 95% Confidence Interval.

Table S1 : Clinical codes used to define cardiovascular diseases
25r each condition, a list of diagnostic codes from was compiled to identify diagnoses based on the coding schemes in use in each data source (International Classification of Diseases, tenth revision (ICD-10) for diagnoses recorded in secondary care; ICD-9 (in use until 31/12/2000) and ICD-10 for diagnoses recorded on death certificates (used in sensitivity analyses only); UK Office of Population Census and Surveys classification (OPCS-4) for procedures performed in secondary care settings; and CPRD Aurum and CPRD Gold code dictionaries for primary care data, which include a combination of Read, SNOMED, and local EMIS codes.25

Table S3 : Characteristics of patients with incident cardiovascular disease between 2000 and 2019, stratified by age at diagnosis
Patient characteristics at the time of their first cardiovascular disease diagnosis.Socioeconomic status was defined as the Index of Multiple Deprivation (IMD) 2015 quintile, with SES 1 referring to the most affluent and SES 5 to the most deprived socioeconomic quintile.Blood pressure, body mass index, smoking status and cholesterol are presented as the latest measurement within two years prior to first cardiovascular disease (CVD) diagnosis.Comorbidities are presented as the percentage of patients diagnosed with the condition of interest at any time up first CVD diagnosis.Number and percentageof records with missing data are displayed for variables with missing entries.For variables with missing entries, summary statistics present observed values alongside the percentage of missing values.Category percentages refer to complete cases.